It’s the same with math. A lot of people say they don’t need to be able to do basic arithmetic because they can use a calculator. But I think that you can process the world much better and faster if at a minimum you have some intuition about numbers and arithmetic.
It’s the same with a lot of other things. AI and search engines help a lot but you are at an advantage if at least you have some ability to gauge what should be possible and how to do it.
TLDR: firing and forgetting the first result off of a single google search isn't the best long term approach. But this guy had a bri..coaching to sell you that apparently makes you a better human being
> Rowlands et al. wrote about the so called “digital natives” that they lack the critical and analytical thinking skills to evaluate the information they find on the internet.
This doesn't match the cultural shift in the last 20 years. A generation of people grew up with chat rooms and immediately discovered the ability to misrepresent oneself on the internet. "On the internet, no one knows you're a dog", as they say. That whole demographic assumes that media is lying by default. Compare that to previous generations that trusted certain media institutions like cable news, newspapers, radio shows, etc. because the production value and scarcity of media instilled trust.
Trust in media institutions is at an all time low, and will likely never recover. That has to be attributed to the newer generations. They are more skeptical of propaganda than ever before. To them, the high production value media outlets are just a quaint legacy variety of content slop.
> That has to be attributed to the newer generations. They are more skeptical of propaganda than ever before. To them, the high production value media outlets are just a quaint legacy variety of content slop.
Right. The skeptical newer generation knows better. It's the generation that is immune to influence. They're so resistant to it that they've finally driven advertisers to realize that spamming YouTube, IG, TikTok, with ads peddling some new hype every week is pointless.
Sarcasm aside, the newer generation, in any generation, is always as naive as they're said to be. You're not born with wisdom and your parents can't save you from the candle fire, no matter how much they try. Sooner or later, you'll have to burn that finger to learn. Life is an experience game. No way around it.
> If you can’t produce a comprehensive answer with confidence and on the whim the second you read the question, you don’t have the sufficient background knowledge.
While the article makes some reasonable points, this is too far gone. You don't need to know how to "weigh each minute spend on flexibility against the minutes spent on aerobic capacity and strength" to put together a reasonable workout plan. Sure, your workouts might not be as minmaxed as they possibly could be, but that really doesn't matter. So long as the plan is not downright bad, the main thing is that you keep at it regularly. The same idea extends to nearly every other domain, you don't need to be a deep expert to get reasonably good results.
I remember things just fine, just not at the sufficient detail to remember all aspects at the drop of a pin. What I hold on to are the core concepts that allow me to hit the ground running when I have to interact with the subject-matter again.
I remember the dotCom bubble. After the bubble burst, people got on with putting storefronts and other kinds of business on-line in a more sober fashion.
I predict the same thing will happen with the current AI tools: the bubble will burst, a bunch of folks will lose their shirts, and the world in general will come to a more realistic and sober understanding of what they are good for. We will figure out how to provide the useful parts without massive data centers and it will become natural. (I remember when things a graphics card can do trivially required a supercomputer with supporting staff.)
> Looks good alright? Or does it? How do you know? You can’t if you don’t have sufficient background knowledge … If you can’t produce a comprehensive answer with confidence and on the whim the second you read the question, you don’t have the sufficient background knowledge.
> “I just ask ChatGPT for that, too!”, the AI generation might ask. Ok, and then what? How can you assess the answers … you are taking on an impossible task, because you can’t use enough of your brain for your cognitive operations.
So it’s Zeno’s paradox of knowing stuff?
It can’t be impossible to know things, you’ve just got to decide when you know enough to get going on. Otherwise you’re mired in analysis paralysis and you never get anything done.
I do agree that deep knowledge of the foundations a subject - particularly a skilled practice or craft - is a path to proficiency and certainly a requirement for mastery. But there are plenty of times when you can get away with ‘just reading the documentation’ and doing as instructed.
You do not first need to invent the universe in order to begin exercising, you can just start talking a 20 minute walk after lunch.
> The reduced engagement with the material reduces the emotional weight of the whole line of action. You mind is an engine that is fuelled by emotion. Without any emotion, you don’t think. Rather, you try to imitate thinking efficiently.
This doesn't sound true and they don't seem to offer any support for the claim.
There's a whole host of emotion-driven cognitive biases, where an effective counter is to reduce the emotional weight of the whole line of action.
Of course, to their credit, it's only by remembering those biases that I could see their error
> You have to remember EVERYTHING. Only then you can perform the cognitive tasks necessary to perform meaningful knowledge work.
You don't have to remember everything. You have to remember enough entry points and the shape of what follows, trained through experience and going through the process of thinking and writing, to reason your way through meaningful knowledge work.
This is task-specific. Consider having a conversation in a foreign language. You don't have time to use a dictionary, so you must have learned words to be able to use them. Similarly for other live performances like playing music.
When you're writing, you can often take your time. Too little knowledge, though, and it will require a lot of homework.
I don't think that the point of the article was "you are dumb if you don't remember absolutely everything".
The point, I believe, was that the more you remember, the better you can think. As in you should strive to remember stuff, and not just be lazy and rely on LLMs. I agree with that.
As someone who can answer all of those questions about the workout plan in depth, it’s not a bad plan. It’s actually quite good. Missing a little detail but that’s OK.
Surprised this is on top. Human beings have been using tools to augment memory since writing. Lots of these tools are faulty but then so too is memory.
If you want to remember everything good luck, but I am not convinced.
One thing that I like is that things are much easier in person. When someone shows me an AI overview they just googled on their phone, I can say "I don't think that's true." Then we can discuss. The more we talk about the subject, the more we develop our knowledge. It's not black and white.
A great deal of anti-AI posts as of late seem like milquetoast pearl clutching to me. They don’t want to outright say they feel threatened/devalued but the arguments they put forward are not only unconvincing, but in this case among many others actively work against them.
nb. I tried really hard to not point out the smugness of Zettelkasten which I suspect emboldens this feeling of superiority, because I’d rather sit this one out and see how it goes. Something tells me the AI will win by a landslide.
You definitely do not need to remember everything, it’s not worth the effort to try, famously in programming even the best look up things they have looked up before.
Memory is helpful but brains aren’t hard drives, they aren’t designed to store information perfectly.
The irony here is using fitness as an example of knowable things.
Fitness guidelines is very much not a settled science, and is highly variable per individual beyond the very basics (to lose weight eat fewer calories than you burn, to build muscle you should lift heavy things).
For every study saying that 8-12 reps x3 is the optimal muscle growth strategy there is another saying that 20x2 is better, and a third saying that 5x5 is better. If you want to know how much protein you should eat to gain muscle mass, good luck; most studies have settled on 1.6g/kg per day as the maximum amount that will have an effect, but you can find many reputable fitness sources suggesting double that.
You can memorize "facts", but they will change as the state of the art changes... or is Pluto still a planet?
The ability to parse information and sources, as well as knowing the limits of your knowledge is far more important than memorizing things.
I agree with the point being made, even if it is taken to an extreme. I would say you don't need to remember everything, but you do need to have been exposed to it. Not knowing what you don't know is a huge handicap in knowledge work.
“Try to learn something about everything and everything about something.”
I am sympathetic to memory-focused tools like Anki and Zettelkasten (haven't used the latter myself, though) but I think this post is a bit oversimplified.
I think there are at least two models of work that require knowledge:
1. Work when you need to be able to refer to everything instantly. I don't know if this is actually necessary for most scenarios other than live debates, or some form of hyper-productivity in which you need to have extremely high-quality results near-instantaneously.
(HN comments are, amusingly, also an example – comments that are in-depth but come days later aren't relevant. So if you want to make a comment that references a wide variety of knowledge, you'll probably need to already know it, in toto.)
2. Work when you need to "know a small piece of what you don't remember as a whole", or in other terms, know the map, but not necessarily the entire territory. This is essentially most knowledge work: research, writing, and other tasks that require you to create output, but that output doesn't need to be right now, like in a debate.
For example, you can know that X person say something important about Y topic, but not need to know precisely what it was – just look it up later. However, you do still need to know what you're looking for, which is a kind of reference knowledge.
--
What is actually new lately, in my experience, is that AI tools are a huge help for situations where you don't have either Type 1 or Type 2 knowledge of something, and only have a kind of vague sense of the thing you're looking for.
Google and traditional search engines are functionally useless for this, but asking ChatGPT a question like, "I am looking for people that said something like XYZ." This previously required someone to have asked the exact same question on Reddit/a forum, but now you can get a pretty good answer from AI.
> What is actually new lately, in my experience, is that AI tools are a huge help for situations where you don't have either Type 1 or Type 2 knowledge of something
IMO, this is the whole point of the article: AI tools "help" a lot when we are completely uninformed. But in doing that, they prevent us from getting informed in the first place. Which is counter-productive in the long term.
I like to say that learning goes in iterations:
* First you accept new material (the teacher shows some mathematical concept and proves that it works). It convinces you that it makes sense, but you don't know enough to actually be sure that the proof was 100% correct.
* Then you try to apply it, with whatever you could memorise from the previous step. It looked easy when the teacher did it, but when you do it yourself it raises new questions. But while doing this, you memorise it. Being able to say "I can do this exercise, but in this other one there is this difference and I'm stuck" means that you have memorised something.
* Now that you have memorised more, you can go back to the material, and try to convince yourself that you now see how to solve that exercise you were stuck with.
* etc.
It's a loop of something like "accept, understand, memorise, use". If, instead, you prompt until the AI gives you the right answer, you're not learning much.
"IMO, this is the whole point of the article: AI tools "help" a lot when we are completely uninformed. But in doing that, they prevent us from getting informed in the first place. Which is counter-productive in the long term."
Great way of framing it - simple and cuts straight to the heart of the issue.
While I agree with the gist of the article, I think the AI example is poor, because we know AI can make stuff up and it's a problem. So this failure of AI to be reasonably correct weakens the argument. In the old days, you would rely on an expert (through say a book, like encyclopedia) to tell you this. The issue then becomes who you trust.
I would say your own knowledge is like a memory cache. If you know stuff, then the relevant work becomes order of magnitudes faster. But you can always do some research and get other stuff in the cache.
(Human mind is actually more than a cache because you also create mental models, which typically stay with you. So it's easier to pickup details after they get evicted, because the mental model is kept. I think the goal of memorising stuff in school should be exactly that - forget all the details, but in the learning process build a good mental model that you have for life.)
It's like with math. You could theoretically only memorize the axioms and rederive everything else on the fly.
But in practice, you don't have enough working memory or processing power to do that, so you'd be stuck with the math a few derivation steps above the axioms only.
To actually use math for problem solving, you need to memorize everything up to the bleeding edge, and to train yourself to operate on intermediate-level abstractions intuitively.
I was talking with somebody about their migration recently [0], and we got to speculating about AI and how it might have helped. There were basically 2 paths:
- Use the AI and ask for answers. It'll generate something! It'll also be pleasant, because it'll replace the thinking you were planning on doing.
- Use the AI to automate away the dumb stuff, like writing a bespoke test suite or new infra to run those tests. It'll almost certainly succeed, and be faster than you. And you'll move onto the next hard problem quickly.
It's funny, because these two things represent wildly different vibes. The first one, work is so much easier. AI is doing the job. In the second one, work is harder. You've compressed all your thinking work, back-to-back, and you're just doing hard thing after hard thing, because all the easy work happens in the background via LLM.
If you're in a position where there's any amount of competition (like at work, typically), it's hard to imagine where the people operating in the 2nd mode don't wildly outpace the people operating in the first, both in quality and volume of output.
But also, it's exhausting. Thinking always is, I guess.
regarding #2: "Automate the dumb/boring stuff", I always think of the big short when Michael Burry said "yes I read all the boring spreadsheets, and I now have a contrary position". And ended up being RIGHT.
For example, I believe writing unit tests is way too important to be fully relegated to the most junior devs, or even LLM generation! In other fields, "test engineer" is an incredibly prestigious position to have, for example "lead test engineer, Space X/ Nasa/etc" -- that ain't a slouch job, you are literally responsible for some of the most important validation and engineering work done at the company.
So I do question the notion that we can offload the "simple" stuff and just move on with life. It hasn't really fully worked well in all fields, for example have we really outsourced the boring stuff like manufacturing and made things way better? The best companies making the best things do typically vertically integrate.
The problem with LLMs is that they are not good enough to do the dumb stuff by themselves. and they are still so dumb that they will bias you once you have to intervene.
But this is the idea behind compilers, type checkers, automated testing, version control, and etc. It's perfectly valid.
At my first software dev internship my manager asked me to code in languages I was not trained in. I told him I would need some time to study up on these. He scoffed and said just look up what you need on Google. Initially I resisted, I felt like it was too shallow. That it was akin to copying answers I didn't really comprehend. However it didn't take long to pick up the habit. Learning is like going to the gym now, it's self enforced discipline
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[ 2.9 ms ] story [ 68.4 ms ] threadIt’s the same with a lot of other things. AI and search engines help a lot but you are at an advantage if at least you have some ability to gauge what should be possible and how to do it.
This doesn't match the cultural shift in the last 20 years. A generation of people grew up with chat rooms and immediately discovered the ability to misrepresent oneself on the internet. "On the internet, no one knows you're a dog", as they say. That whole demographic assumes that media is lying by default. Compare that to previous generations that trusted certain media institutions like cable news, newspapers, radio shows, etc. because the production value and scarcity of media instilled trust.
Trust in media institutions is at an all time low, and will likely never recover. That has to be attributed to the newer generations. They are more skeptical of propaganda than ever before. To them, the high production value media outlets are just a quaint legacy variety of content slop.
Right. The skeptical newer generation knows better. It's the generation that is immune to influence. They're so resistant to it that they've finally driven advertisers to realize that spamming YouTube, IG, TikTok, with ads peddling some new hype every week is pointless.
Sarcasm aside, the newer generation, in any generation, is always as naive as they're said to be. You're not born with wisdom and your parents can't save you from the candle fire, no matter how much they try. Sooner or later, you'll have to burn that finger to learn. Life is an experience game. No way around it.
While the article makes some reasonable points, this is too far gone. You don't need to know how to "weigh each minute spend on flexibility against the minutes spent on aerobic capacity and strength" to put together a reasonable workout plan. Sure, your workouts might not be as minmaxed as they possibly could be, but that really doesn't matter. So long as the plan is not downright bad, the main thing is that you keep at it regularly. The same idea extends to nearly every other domain, you don't need to be a deep expert to get reasonably good results.
That said AI, Search and the like can be quite useful and helpful.
I predict the same thing will happen with the current AI tools: the bubble will burst, a bunch of folks will lose their shirts, and the world in general will come to a more realistic and sober understanding of what they are good for. We will figure out how to provide the useful parts without massive data centers and it will become natural. (I remember when things a graphics card can do trivially required a supercomputer with supporting staff.)
> “I just ask ChatGPT for that, too!”, the AI generation might ask. Ok, and then what? How can you assess the answers … you are taking on an impossible task, because you can’t use enough of your brain for your cognitive operations.
So it’s Zeno’s paradox of knowing stuff?
It can’t be impossible to know things, you’ve just got to decide when you know enough to get going on. Otherwise you’re mired in analysis paralysis and you never get anything done.
I do agree that deep knowledge of the foundations a subject - particularly a skilled practice or craft - is a path to proficiency and certainly a requirement for mastery. But there are plenty of times when you can get away with ‘just reading the documentation’ and doing as instructed.
You do not first need to invent the universe in order to begin exercising, you can just start talking a 20 minute walk after lunch.
This doesn't sound true and they don't seem to offer any support for the claim.
There's a whole host of emotion-driven cognitive biases, where an effective counter is to reduce the emotional weight of the whole line of action.
Of course, to their credit, it's only by remembering those biases that I could see their error
You don't have to remember everything. You have to remember enough entry points and the shape of what follows, trained through experience and going through the process of thinking and writing, to reason your way through meaningful knowledge work.
When you're writing, you can often take your time. Too little knowledge, though, and it will require a lot of homework.
1- You may remember only the initial state and the brain does the rest, like with mnemonics
2- You may remember only the initial steps towards a solution, like knowing the assumptions and one or two insights to a mathematical proof?
I'd say a Zettlekasten user would agree with you if you mean 1
The point, I believe, was that the more you remember, the better you can think. As in you should strive to remember stuff, and not just be lazy and rely on LLMs. I agree with that.
Wasn’t a good example for me.
If you want to remember everything good luck, but I am not convinced.
But online? @grok is this true?
nb. I tried really hard to not point out the smugness of Zettelkasten which I suspect emboldens this feeling of superiority, because I’d rather sit this one out and see how it goes. Something tells me the AI will win by a landslide.
Memory is helpful but brains aren’t hard drives, they aren’t designed to store information perfectly.
Fitness guidelines is very much not a settled science, and is highly variable per individual beyond the very basics (to lose weight eat fewer calories than you burn, to build muscle you should lift heavy things).
For every study saying that 8-12 reps x3 is the optimal muscle growth strategy there is another saying that 20x2 is better, and a third saying that 5x5 is better. If you want to know how much protein you should eat to gain muscle mass, good luck; most studies have settled on 1.6g/kg per day as the maximum amount that will have an effect, but you can find many reputable fitness sources suggesting double that.
You can memorize "facts", but they will change as the state of the art changes... or is Pluto still a planet?
The ability to parse information and sources, as well as knowing the limits of your knowledge is far more important than memorizing things.
“Try to learn something about everything and everything about something.”
I think there are at least two models of work that require knowledge:
1. Work when you need to be able to refer to everything instantly. I don't know if this is actually necessary for most scenarios other than live debates, or some form of hyper-productivity in which you need to have extremely high-quality results near-instantaneously.
(HN comments are, amusingly, also an example – comments that are in-depth but come days later aren't relevant. So if you want to make a comment that references a wide variety of knowledge, you'll probably need to already know it, in toto.)
2. Work when you need to "know a small piece of what you don't remember as a whole", or in other terms, know the map, but not necessarily the entire territory. This is essentially most knowledge work: research, writing, and other tasks that require you to create output, but that output doesn't need to be right now, like in a debate.
For example, you can know that X person say something important about Y topic, but not need to know precisely what it was – just look it up later. However, you do still need to know what you're looking for, which is a kind of reference knowledge.
--
What is actually new lately, in my experience, is that AI tools are a huge help for situations where you don't have either Type 1 or Type 2 knowledge of something, and only have a kind of vague sense of the thing you're looking for.
Google and traditional search engines are functionally useless for this, but asking ChatGPT a question like, "I am looking for people that said something like XYZ." This previously required someone to have asked the exact same question on Reddit/a forum, but now you can get a pretty good answer from AI.
That might be a good criteria for how much to memorize: do you want to be able to do it live?
IMO, this is the whole point of the article: AI tools "help" a lot when we are completely uninformed. But in doing that, they prevent us from getting informed in the first place. Which is counter-productive in the long term.
I like to say that learning goes in iterations:
* First you accept new material (the teacher shows some mathematical concept and proves that it works). It convinces you that it makes sense, but you don't know enough to actually be sure that the proof was 100% correct.
* Then you try to apply it, with whatever you could memorise from the previous step. It looked easy when the teacher did it, but when you do it yourself it raises new questions. But while doing this, you memorise it. Being able to say "I can do this exercise, but in this other one there is this difference and I'm stuck" means that you have memorised something.
* Now that you have memorised more, you can go back to the material, and try to convince yourself that you now see how to solve that exercise you were stuck with.
* etc.
It's a loop of something like "accept, understand, memorise, use". If, instead, you prompt until the AI gives you the right answer, you're not learning much.
Great way of framing it - simple and cuts straight to the heart of the issue.
I would say your own knowledge is like a memory cache. If you know stuff, then the relevant work becomes order of magnitudes faster. But you can always do some research and get other stuff in the cache.
(Human mind is actually more than a cache because you also create mental models, which typically stay with you. So it's easier to pickup details after they get evicted, because the mental model is kept. I think the goal of memorising stuff in school should be exactly that - forget all the details, but in the learning process build a good mental model that you have for life.)
But in practice, you don't have enough working memory or processing power to do that, so you'd be stuck with the math a few derivation steps above the axioms only.
To actually use math for problem solving, you need to memorize everything up to the bleeding edge, and to train yourself to operate on intermediate-level abstractions intuitively.
- Use the AI and ask for answers. It'll generate something! It'll also be pleasant, because it'll replace the thinking you were planning on doing.
- Use the AI to automate away the dumb stuff, like writing a bespoke test suite or new infra to run those tests. It'll almost certainly succeed, and be faster than you. And you'll move onto the next hard problem quickly.
It's funny, because these two things represent wildly different vibes. The first one, work is so much easier. AI is doing the job. In the second one, work is harder. You've compressed all your thinking work, back-to-back, and you're just doing hard thing after hard thing, because all the easy work happens in the background via LLM.
If you're in a position where there's any amount of competition (like at work, typically), it's hard to imagine where the people operating in the 2nd mode don't wildly outpace the people operating in the first, both in quality and volume of output.
But also, it's exhausting. Thinking always is, I guess.
[0] Rijnard, about https://sourcegraph.com/blog/how-not-to-break-a-search-engin...
For example, I believe writing unit tests is way too important to be fully relegated to the most junior devs, or even LLM generation! In other fields, "test engineer" is an incredibly prestigious position to have, for example "lead test engineer, Space X/ Nasa/etc" -- that ain't a slouch job, you are literally responsible for some of the most important validation and engineering work done at the company.
So I do question the notion that we can offload the "simple" stuff and just move on with life. It hasn't really fully worked well in all fields, for example have we really outsourced the boring stuff like manufacturing and made things way better? The best companies making the best things do typically vertically integrate.
But this is the idea behind compilers, type checkers, automated testing, version control, and etc. It's perfectly valid.